InfiAgent: A Multi-Tool Agent for AI Operating Systems

Speaker: Dr. Hongxia Yang

Title:  "InfiAgent: A Multi-Tool Agent for AI Operating Systems"

Date:   Wednesday; 13 March 2024

Time:   2:30pm - 3:30pm

Venue:  Room 2405 (via lift 17/18), HKUST


Following the launch of GPT4-Agent, GPT4 has demonstrated its flexibility 
in utilizing tools like Advanced Data Analytics (ADA, previously known as 
code interpreter) and DALLE3, although the details of GPT4-Agent have not 
been fully disclosed. Over the past years, we have intensively studied the 
core functionalities of GPT4, progressively developing a system comparable 
to GPT4-Agent, named InfiAgent1. Initially, we replicated Codex and 
discovered that while existing models such as CodeLlama, StarCoder, and 
WizardCoder excel in programming capabilities, they fall short in handling 
FreeformQA problems for coding. To address this, we created InfiCoder--the 
first open-source model capable of handling text-tocode, code-to-code, and 
freeform code-related QA tasks simultaneously. Building on this, we 
developed InfiCoder-Eval 2 (FreeformQA benchmark), which includes 270 
high-quality automated test questions. Our findings indicate that even 
GPT4 has room for improvement in this area (achieving a score rate of only 
59.13%). Based on InfiCoder, we launched the InfiAgent framework, focusing 
on the field of data analysis. This framework first defines the problem 
framework and evaluation objectives for data analysis. Then, in line with 
the data analysis scenarios, we developed a specialized Agent system based 
on the React format and LLM, effectively addressing data analysis 
challenges. This system integrates an LLM with programming capabilities 
and a sandbox environment for executing Python code, generating solutions 
and corresponding code through multiple rounds of dialogues. It is the 
industry's first Agent framework closest to the capabilities of ADA. 
Additionally, we expanded the application scenarios of InfiAgent, 
multimodal LLM (MLLM) reasoning tool InfiMM 3, achieving excellent 
results. Among the open-source models, InfiMM performs the best on the 
MMMU leaderboard 4 with the smallest size of only 7B. Particularly in MLLM 
reasoning, we found that there is significant room for improvement in the 
current GPT4V (achieving a score rate of only 74.44% 5). These 
achievements not only reveal the tremendous potential of InfiAgent but 
also showcase our possible directions in surpassing the capabilities of 


Dr. Hongxia Yang, PhD from Duke University, has published over 100 papers 
in top-tier conferences and journals, and holds more than 50 patents in 
the USA and China. Her contributions to the field have been recognized 
through numerous prestigious awards, including the coveted Super AI Leader 
(SAIL) Award at the 2019 World Artificial Intelligence Conference, the 
Second- Class National Science and Technology Progress Award in 2020, 
which is one of China's highest technological honors, the First-Class 
Science and Technology Progress Award from the Electronics Society in 2021 
and the First-Class Science and Technology Progress Award from the 
Ministry of Education in 2022. Forbes China lauded her as one of the Top 
50 Women in Tech in 2022, a testament to her trailblazing role in the tech 
industry. Currently, she serves as the Head of Large Models at ByteDance 
US. Previously, she worked as a research staff member at IBM T.J. Watson 
research center, principal scientist at Yahoo!, an AI scientist and 
director at Alibaba DAMO Academy, and an adjunct professor at Zhejiang 
University's Shanghai Advanced Research Institute.